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Persistent Identifier
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doi:10.18710/LSVFOL |
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Publication Date
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2026-02-19 |
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Title
| Replication Data for: Damage detection in chain and synthetic mooring lines of Floating Offshore Wind Turbines |
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Author
| Sakaris, Christoshttps://ror.org/02gagpf75ORCIDhttps://orcid.org/0000-0002-7211-4520
Schlanbusch, Runehttps://ror.org/02gagpf75ORCIDhttps://orcid.org/0000-0002-0730-845X
Lee, Chern Fonghttps://ror.org/02qte9q33ORCIDhttps://orcid.org/0000-0001-7397-6002
Ong, Muk Chenhttps://ror.org/02qte9q33ORCIDhttps://orcid.org/0000-0001-5288-5857
Tutkun, Murathttps://ror.org/02jqtg033ORCIDhttps://orcid.org/0000-0003-0702-5051
Nygaard, Tor Andershttps://ror.org/02jqtg033ORCIDhttps://orcid.org/0000-0001-6803-3763 |
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Point of Contact
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Use email button above to contact.
Sakaris, Christos (NORCE Research AS) |
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Description
| About 65 GW of onshore wind turbine installations in Europe will reach end-of-design-life by 2028. It is time for the operators to decide on one of the three end-of-life scenarios, namely, decommissioning, lifetime extension, or repowering. The last two options will increase the operating life and thus reduce lifecycle costs. These end-of-life decisions require careful consideration of the accumulated fatigue life of each turbine in a wind farm to minimize monetary risk for the wind farm operators. Today, this decision is primarily based on a single point assessment by the certification authority. AIMWind (Analytics for asset Integrity Management of Windfarms) project (https://www.aimwind.no/) proposes a continuous evaluation of wind farm health based on big data analytics using multimodal data such as wind, operational data, weather, condition monitoring, and inspection logs across a wind farm. Conventional approaches to fatigue estimation are slow and inadequate to achieve these goals, especially in large wind farms. Such a continuous health assessment will facilitate not only accurate life predictions but also continuous improvement of wind turbine operations to ensure long life and high availability. In the context of the AIMWind project, simulated acceleration data representing the healthy and damaged chain or synthetic mooring lines of Floating Offshore Wind Turbines have been generated. The data have been used for the validation of various machine learning methods used for damage detection in the considered chain and synthetic mooring lines. (2026-02-12) |
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Subject
| Engineering |
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Keyword
| Mooring lines
Chain
Synthetic
Acceleration
Damage
AIMWind |
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Related Publication
| References: Christos Sakaris, Nikolaos Anastasiadis, Rune Schlanbusch, Surya Kandukuri, Sensitivity analysis for multi-measurement points based SHM in the mooring lines of floating offshore wind turbines, in: Proceedings of the 21st European Energy Research Alliance (EERA) DeepWind conference, Trondheim, Norway, 2024. Journal of Physics: Conference Series, 2875, 012032, 2024. url https://iopscience.iop.org/article/10.1088/1742-6596/2875/1/012032
Nikolaos Anastasiadis, Christos Sakaris, Rune Schlanbusch, John Sakellariou, Vibration-Based SHM in the Synthetic Mooring Lines of the Semi-Submersible OO-Star Wind Floater Under Varying Environmental and Operational Conditions. Sensors, 24(2), 543, 2024. doi 10.3390/s24020543 https://doi.org/10.3390/s24020543
References: Christos Sakaris, Rune Schlanbusch, Tor Anders Nygaard, John Sakellariou, Murat Tutkun, Statistical times series based damage detection in the fiber rope mooring lines of the semi-submersible OO-STAR wind floater, in: Proceedings of the 62nd IEEE Conference on Decision and Control (CDC), Marina Bay Sands, Singapore, 2023, pp. 4861-4866. doi 10.1109/CDC49753.2023.10383638 https://doi.org/10.1109/CDC49753.2023.10383638
References: Xenophon Konstantinou, Kyriakos Kritikakos, Chern Fong Lee, Christos Sakaris, John Sakellariou, Rune Schlanbusch, Muk Chen Ong, Vibration-based structural health monitoring of the mooring lines in a floating offshore wind turbine under varying environmental conditions: NN vs STS based methods, in: Proceedings of the 31st International Conference on Noise and Vibration Engineering (ISMA), Leuven, Belgium, 2024. isbn 979-8-3313-0812-4 |
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Language
| English |
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Producer
| Schlanbusch, Rune (NORCE Research AS) |
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Production Location
| Stavanger; Kjeller |
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Contributor
| Data Manager: NORCE Research AS
Data Collector: Institute for Energy (IFE)
Data Collector: University of Stavanger |
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Funding Information
| Research Council of Norway: Grant number: 312486 |
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Distributor
| DataverseNO (NORCE Research AS) https://dataverse.no |
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Depositor
| Sakaris, Christos |
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Deposit Date
| 2026-02-12 |
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Time Period
| Start Date: 2023-03-31; End Date: 2023-03-31
Start Date: 2023-04-01; End Date: 2023-04-01 |
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Date of Collection
| Start Date: 2023-03-31; End Date: 2023-03-31
Start Date: 2023-04-01; End Date: 2023-04-01 |
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Data Type
| Simulated acceleration data |
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Software
| 3DFloat
SIMA |
